Highway Segments Research Articles

Investigation of the Impact of the Guangzhou-Lechang Expressway Segment at the Pajiang River Detention Basin to Its Normal Flood Prevention Operation

Based on the existing relevant information, the paper investigates the occupation situation of the segment of the Guangzhou-Lechang expressway in the Pajiang River detention basin and analyzes the relationship of the water conservancy facility construction to the planning of the detention basin. One-dimensional and two-dimensional mathematical models are established to evaluate the construction effect on flood control in the detention basin under the normal use condition of the Pajiang River. The results show that the construction occupies 0.09% regulation and storage volume of the detention basin, which leads to the following impacts to its flood prevention operation: the starting time of the detention basin delayed 3~12 minutes, the average discharge of the Jiangkouxun section in the main river of the Beijiang River increased 12m3/s, and the average discharges of the Tiechuang-ao section of the Pajiang River and the Changbu section of the Yuantan River in the detention basin decreased 14 m³/s and 13 m³/s, respectively. Meanwhile, the highest water level of the Jiangkouxu Station and the peak discharge of the Shijiao Station remain almost the same. Therefore, as the conclusion, the engineering construction of the expressway segment only produces a minor impact on flood control of the Beijiang River. This investigation provides the technical basis for evaluation of the impact to flood prevention by highway construction for similar projects and will be useful for decision-making of the relevant planning agencies.

Analyzing the Impact of Roadmap and Vehicle Features on Electric Vehicles Energy Consumption

Electric Vehicles (EVs) market penetration rate is continuously increasing due to several aspects such as pollution reduction initiatives, government incentives, cost reduction, and fuel cost increase, among others. In the vehicular field, researchers frequently use simulators to validate their proposals before implementing them in real world, while reducing costs and time. In this work, we use our ns-3 network simulator enhanced version to demonstrate the influence of the map layout and the vehicle features on the EVs consumption. In particular, we analyze the estimated consumption of EVs simulating two different scenarios: (i) a segment of the E313 highway, located in the north of Antwerp, Belgium and (ii) the downtown of the city of Antwerp with real vehicle models. According to the results obtained, we demonstrate that the mass of the vehicle is a key factor for energy consumption in urban scenarios, while in contrast, the Air Drag Coefficient ( ${\text C}_{d}$ ) and the Front Surface Area (FSA) play a critical role in highway environments. The most popular and powerful simulations tools do no present combined features for mobility, realistic map-layouts and electric vehicles consumption. As ns-3 is one of the most used open source based simulators in research, we have enhanced it with a realistic energy consumption feature for electric vehicles, while maintaining its original design and structure, as well as its coding style guides. Our approach allows researchers to perform comprehensive studies including EVs mobility, energy consumption, and communications, while adding a negligible overhead.

Developing a macroscopic model based on fuzzy cognitive map for road traffic flow simulation

Fuzzy cognitive maps (FCM) have been broadly employed to analyze complex and decidedly uncertain systems in modeling, forecasting, decision making, etc. Road traffic flow is also notoriously known as a highly uncertain nonlinear and complex system. Even though applications of FCM in risk analysis have been presented in various engineering fields, this research aims at modeling road traffic flow based on macroscopic characteristics through FCM. Therefore, a simulation of variables involved with road traffic flow carried out through FCM reasoning on historical data collected from the e-toll dataset of Hungarian networks of freeways. The proposed FCM model is developed based on 58 selected freeway segments as the “concepts” of the FCM; moreover, a new inference rule for employing in FCM reasoning process along with its algorithms have been presented. The results illustrate FCM representation and computation of the real segments with their main road traffic-related characteristics that have reached an equilibrium point. Furthermore, a simulation of the road traffic flow by performing the analysis of customized scenarios is presented, through which macroscopic modeling objectives such as predicting future road traffic flow state, route guidance in various scenarios, freeway geometric characteristics indication, and effectual mobility can be evaluated.

A Control-Theoretic Approach for Scalable and Robust Traffic Density Estimation Using Convex Optimization

Monitoring and control of traffic networks represent alternative, inexpensive strategies to minimize traffic congestion. As the number of traffic sensors is naturally constrained by budgetary requirements, real-time estimation of traffic flow in road segments that are not equipped with sensors is of significant importance - thereby providing situational awareness and guiding real-time feedback control strategies. To that end, firstly we build a generalized traffic flow model for stretched highways with arbitrary number of ramp flows based on the Lighthill Whitham Richards (LWR) flow model. Secondly, we characterize the function set corresponding to the nonlinearities present in the LWR model, and use this characterization to design real-time and robust state estimators (SE) for stretched highway segments. Specifically, we show that the nonlinearities from the derived models are locally Lipschitz continuous by providing the analytical Lipschitz constants. Thirdly, the analytical derivation is then incorporated through a robust SE method given a limited number of traffic sensors, under the impact of process and measurement disturbances and unknown inputs. The estimator is based on deriving a convex semidefinite optimization problem. Finally, numerical tests are given showcasing the applicability, scalability, and robustness of the proposed estimator for large systems under high magnitude disturbances, parametric uncertainty, and unknown inputs.

Output feedback control of two-lane traffic congestion

This paper develops output feedback boundary control to mitigate traffic congestion of an unidirectional two-lane freeway segment. The macroscopic traffic dynamics are described by the Aw–Rascle–Zhang (ARZ) model respectively for both the fast and slow lanes. The traffic density and velocity of each of the two lanes are governed by coupled 2 × 2 nonlinear hyperbolic partial differential equations (PDEs). Lane-changing interactions between the two lanes lead to exchanging source terms between the two pairs second-order PDEs. Therefore, we are dealing with 4 × 4 nonlinear coupled hyperbolic PDEs. Based on driver’s preference for the slow and fast lanes, a reference system of lane-specific uniform steady states in congested traffic is chosen. To stabilize traffic densities and velocities of both lanes to the steady states, two distinct variable speed limits (VSLs) are applied at outlet boundary, controlling the traffic velocity of each lane. Using backstepping transformation, we map the coupled heterodirectional hyperbolic PDE system into a cascade target system, in which the traffic oscillations are damped out through actuation of the velocities at the downstream boundary. Two full-state feedback boundary control laws are developed. We also design a collocated boundary observer for state estimation with sensing of the densities at the outlet. Output feedback boundary controllers are obtained by combining the collocated observer and full-state feedback controllers. The finite time convergence to equilibrium is achieved and a performance improvement in fuel consumption, drivers’ comfort and total travel time is demonstrated with the proposed output feedback controllers for some parameter choice, compared with the open-loop system. The proposed output feedback design is also validated for different congested traffic scenarios and compared with one-lane backstepping and PI control designs.

Who overtakes more? Explanatory analysis of the characteristics of drivers from low/middle and high-income countries on passing frequency

The passing manoeuvre requires a driver to make decisions and take actions which are dependent on his/her behavioural characteristics and driving ability. However, previous works on passing rate models have exclusively considered geometric and traffic-related variables. This study aims at bridging this gap by investigating the influence of driver profile (i.e., age, gender, nationality - Italian or Iranian - aggressive driving scores, driving exposure) on passing frequency. A driving simulation experiment involving 54 drivers (36 Italians, 18 Iranians) was conducted along a 6.67 km segment of a two-lane rural highway with passing manoeuvres permitted along 25% of its length. Controlled factors included traffic flow and speed in the oncoming direction, and speed in the driver direction, with a total of 27 scenarios assigned to drivers based on a 33 confounded factorial design. A Poisson regression model was used to investigate the significance of independent variables. Age and gender and their interaction term were significant, thus the effects of age and gender on the number of passing manoeuvres are mutually interdependent. Furthermore, drivers who drive less often completed fewer overtaking manoeuvres. Sensitivity analyses were carried out to understand the magnitude of change in passing frequency attributable to a variation in the explanatory variables. The findings suggest that driver characteristics have a significant effect on passing frequency and should be considered when conducting a performance and safety evaluation of two-lane roads.

A crash risk identification method for freeway segments with horizontal curvature based on real-time vehicle kinetic response

With the development and maturation of vehicle-based data acquisition technology, in-vehicle data is increasingly being used to explore road safety. This paper reports on research that analyzed the real-time tire force data (kinetic response) obtained from vehicle kinetic experiments, and constructed a new approach for identifying the high-risk of crashes on freeway segments with horizontal curvature. First, the road was divided into 1km units. Then, taking into account the characteristics of freeway alignment, each segment with horizontal curve was selected as the object of subsequent analysis. Automotive instrumentation was used to obtain a measure of tire force in the course of normal driving. The entire data set was preprocessed according to rate of change and the density of the data was reduced. By defining the outliers of the kinetic data and conducting factor analysis, two representative crash risk indicators of longitudinal and lateral stability were obtained. Negative binomial regression model (NBR model) and random effects negative binomial regression model (RENBR model) were constructed and jointly applied based on the new indicators to predict the risk value of horizontal curve segments. The method showed good prediction performance (71.8 %) for high-risk road segments with design flaws, but the predicted effect for low-risk road segments was not ideal. This study not only illustrated the effectiveness of in-vehicle data in assessing road crash risk by coupling multiple kinetic parameters, but also provided support for freeway safety research using surrogate measures of risk when there is a lack of crash statistics.

Impact of CAV Truck Platooning on HCM-6 Capacity and Passenger Car Equivalent Values

Recently, there has been a significant amount of research related to heavy trucks operating as connected and autonomous vehicles (CAVs). As argued in this paper, to understand the potential impact on the freeway system of CAV technologies, analyses should be conducted using the standard US methodological framework. Consequently, this paper uses the exact Highway Capacity Manual, Sixth Edition (HCM-6) equal capacity passenger car equivalent (EC-PCE) methodology to estimate capacity and EC-PCEs for CAV truck platoons on freeway segments. It was found that EC-PCE values for CAV trucks are, on average, 34.3% lower compared to the values for non-CAV trucks, indicating that CAV platoons can have a positive effect on highway capacity. The amount of decrease is a function of a number of CAV operational assumptions, and these are studied through a sensitivity analysis. This paper demonstrates that the effect of CAV truck platoons can be modeled using the standard HCM-6 approach, and the methodology allows meaningful comparisons so that traffic agencies can better prepare for the adoption of CAV technologies.

Coordinated decisions of discretionary lane change between connected and automated vehicles on freeways: a game theory‐based lane change strategy

Discretionary lane change is an essential part of connected and automated vehicles (CAVs) on freeway segments. Most existing studies were conducted to optimise the individual decision of discretionary lane change of CAVs. However, the effects of motion states and discretionary lane change decisions from the surrounding vehicles via vehicle to vehicle communication were ignored. To address such a problem, a game theory-based lane change strategy is proposed to collaborate and optimise decisions of discretionary lane change between the CAVs. The payoff functions are formulated for three types of decision games and the payoff of each decision is quantitatively calculated considering the state information of surrounding vehicles. The Nash equilibrium is applied to find the optimal decision set for players. A simulation platform of a CAV environment built is used to conduct the simulation experiments. Various metrics are employed to evaluate the proposed strategy, such as total travel delay, surrogate safety measurement and wave number. The results show that the proposed lane change strategy using a game-theoretical approach can effectively improve traffic operation, safety and oscillations compared to the baseline strategy. The proposed lane change strategy can further benefit the implementations of the CAVs.

Lane Group–Based Traffic Model for Assessing On-Ramp Traffic Impact

AbstractOn-ramp merging areas are congestion-prone segments of freeways. Depending on the aggressiveness of the driving population and the congestion level, the speed variance among travel lanes du...

Highway Cost Analysis for Platooning of Connected and Autonomous Trucks

If connected and autonomous vehicles use highways under current lane management policy, only partial benefit can be reaped from the platooning of advanced vehicles. Agencies may have to update the existing policy in the near future because of the regular presence of connected and autonomous vehicles on the highways. The probable management approaches are likely to affect highway construction, rehabilitation, and fuel consumption cost. This paper studied a range of lane management approaches in terms of truck platooning and estimated construction and rehabilitation costs using existing models. It also developed a probabilistic approach of generating varying degrees of vehicle platooning to estimate fuel consumption in a simulated environment of highway segment. The results showed that, compared with a no-platoon approach, most of the approaches considered reduce construction cost but increase rehabilitation cost for a unit length of a highway. Fuel consumption cost decreases due to truck platooning with all possible approaches. If truck platooning is achieved by adding a separate truck-only lane, the fuel consumption benefits are considerably higher.

An efficiency-based capacity formulation: mathematical properties and practical implications

Traditional capacity approaches focus only on the vehicle-carrying ability of a facility; i.e. a freeway segment is characterized by higher speed and volume. Furthermore, these definitions lead to the two-capacity phenomenon which creates confusion among practitioners and researchers over which one is the appropriate value. To address these issues, this paper puts forth a new understanding of freeway capacity by considering the optimal traffic state idea where the infrastructure is operating in the most efficient way. This paper endeavors to explain the existence of the optimal traffic state that may lead to a substantial amelioration of traffic conditions on freeways. These analytical properties are transferable to different families of speed–density models. A simulation study is then undertaken to demonstrate how additional benefits can be achieved using the efficiency-based approach. Results show that the proposed methodology improves the overall traffic network performance in comparison to when conventional capacity is used.

Study of the degree of atmospheric air pollution on a segment of the city highway

The article presents the results of experimental and theoretical studies, as a result of which the impact of traffic organization on the level of air pollution from traffic flows was assessed. The studies were carried out on several sections of one segment of the city highway. The assessment of the state of atmospheric air pollution on a highway with heavy traffic (Mira Avenue, Omsk) was carried out on the basis of determining the content of carbon monoxide (CO) in the air, as one of the main components of the exhaust gases of the traffic flow. Calculations are graphically presented that confirm the unevenness of the amount of pollutant emissions by one passenger car that moves along a segment of a city highway, taking into account several options for traffic regime. Modes of movement are due to the organization of road traffic.

Factors influencing driver behaviour along curved merging interchange terminals

Problem statement. To promote safe and comfortable driving operations, design manuals and policies call for merging terminal ramps to be built along straight roadway sections. However, this prescription may need to be overlooked when there is no alternative to terminals being designed along curves. This study aims to assess the impact of design factors on driver behaviour along curved acceleration terminals with continue or reverse curvature prior to joining the motorway. Methodology. A driving simulation experiment was conducted to observe longitudinal and transversal driver behaviour when certain factors (i.e., radius, ramp length, motorway curve direction, and traffic conditions along the motorway segment) were manipulated. The forty-eight drivers involved were separated into groups based on age and gender. Results. The motorway radius has a significant impact on longitudinal performance, while traffic volume impacts on the merging point where vehicles enter the adjoining motorway lane. Compared to linear terminals, the merging abscissa need to be longer on curved ones so as to compensate for the blind spot and enable drivers to identify and accept gaps between vehicles in the traffic flow. The ramp-terminal connection type influences the speed at the beginning of the terminal, the position of the merging point and transversal driver behaviour. Conclusions. Although some limitations in the study were necessary to limit their impact on the experimental design, the results here may prove useful when considering the adoption of appropriate engineering countermeasures at the design stage of entry terminals along curved motorway segments.

Using PAVER Software to Evaluate Pavement Condition Index of Highway Segment in the North Sector of Najaf City (Case Study)

Current paper present part of comprehensive case study for urban highway networks of north sector of Najaf city located 161 km Southwest of Baghdad the capital of Iraq. The main goals of current paper are to evaluation pavement condition index denoted as (PCI). Values of PCI acting a main factor on depending maintenance of pavement, selecting economic alternative, increasing development of engineers and technician’s capabilities, in order to reach the best maintenance management process and achieve the best riding level with lowest costs and the shortest travel time. PAVER software uses surveying data to calculate pavement condition index which should be within rating from zero, which mean pavement in failed condition, to 100, that mean excellent pavement condition. Pavement’s condition is providing predicting its maintenance and Rehabilitation requirements. Free issue of PAVER 5.2 software was used to calculate values of PCI for a minor arterial urban highway in north sector of Najaf city, that is the Najaf-Karbala highway located from Al-Askarain Tunnel intersection to the end of Al-Nidaa district, with length (8011m). Manual geometric and types of distresses survey was depended instead of using assessment technologies to consider the type, degree of severity, of distresses for sample unites along the highway. As a results of implementing PAVER 5.2 software depending on field survey data collection, PCI value is equal to 80. That’s mean this part of the selected study section was in good situation.

Evaluating the Effect of Incidents on Freeway Facilities and Updating the Incident Capacity Adjustments for HCM

This paper investigates the effect of incidents on freeway segment capacity. Currently, the Highway Capacity Manual (HCM) provides a look-up table linking the remaining segment capacity fraction during an incident to the total and closed number of lanes on the segment. In reality, segment capacity during an incident will tend to vary over time, with the most severe effects felt early on before any type of response is initiated, with congestion progressively improving as the appropriate incident management actions are implemented. By applying a genetic algorithm calibration method on each incident day and calibrating the incident capacity adjustment factors (CAFs), optimal time-dependent CAFs were derived that best represented the effect of incidents on the freeway segment capacity. By analyzing the optimal CAFs, the strongest relationship was revealed to be between the optimal time-dependent CAF and the temporal progression of the incident. A regression model was developed to represent this behavior. This was formulated in a manner that can directly adjust the current HCM’s fixed CAF values (for a specific lane closure configuration) for modeling incidents both in single day, seed file application, or for an entire year reliability analysis. A portion of WB I-540 in Raleigh, NC was selected as the study area in which the proposed method was tested. Between January 2014 and December 2018, the team identified 22 isolated incidents (away from the recurring congestion period) that closed one or two lanes of traffic, creating a distinct congestion pattern.

Extremum Seeking for Traffic Congestion Control With a Downstream Bottleneck

Abstract This paper develops boundary control for freeway traffic with a downstream bottleneck. Traffic on a freeway segment with capacity drop at outlet of the segment is a common phenomenon that leads to traffic bottleneck problem. The capacity drop can be caused by lane-drop, hills, tunnel, bridge, or curvature on the road. If incoming traffic flow remains unchanged, traffic congestion forms upstream of the bottleneck since the upstream traffic demand exceeds its capacity. Therefore, it is important to regulate the incoming traffic flow of the segment to avoid overloading the bottleneck area. Traffic densities on the freeway segment are described with the Lighthill–Whitham–Richards (LWR) macroscopic partial differential equation (PDE) model. The incoming flow at the inlet of the freeway segment is controlled so that the optimal density that maximizes the outgoing flow is reached and the traffic congestion upstream of the bottleneck is mitigated. The density and traffic flow relation at the bottleneck area, usually described with fundamental diagram, is considered to be unknown. We tackle this problem using extremum seeking (ES) control with delay compensation for the LWR PDE. ES control, a nonmodel-based approach for real-time optimization, is adopted to find the optimal density for the unknown fundamental diagram. A predictor feedback control design is proposed to compensate the delay effect of traffic dynamics in the freeway segment. In the end, simulation results are obtained to validate a desired performance of the controller on the nonlinear LWR model with an unknown fundamental diagram.

Collective Risk Ranking of Highway Segments on the Basis of Severity-Weighted Crash Rates

This study is intended to focus on the major factors affecting traffic crash rates and severity levels, in addition to identifying crash-prone locations (i.e., black spots) based on the two indicators. The available crash data for different road segments used for the analysis were obtained from the Washington state database provided by the Highway Safety Information System (HSIS) for the years 2006 to 2011. A Random Forest (RF) classifier was used to predict the outcome level of crash severity, while crash rates were predicted by applying RF regressor. Certain features were selected for each model besides the abstraction of new features to check if there are unobserved correlations affecting the independent variables, such as accounting for the number and weight of crashes within 1 km2 area by implementing the Getis-Ord Gi∗ index. Moreover, to calculate the collective risk (CR) score, crash rates were adjusted to incorporate crash severity weights (cost per severity type) and regression-to-the-mean (RTM) bias via Empirical Bayes (EB) method. Finally, segments were ranked according to their CR score.

Analyzing intra-metropolitan variation in highway traffic performance in Los Angeles using archived real-time data

ABSTRACT We conduct a case study of highway system performance in Los Angeles County. We use the Los Angeles Archived Data Management System, a comprehensive archive of regional real-time multi-modal transportation system data, to analyze effects of systematic, functional, random, and land use factors on performance variation over different time periods of the day. To understand functional class effects, we use cluster analysis on geometric and demand parameters to identify functionally similar groups of highway segments. We compare performance between groups and across segments within groups. We perform regression analysis to test the influence of various factors on performance. We find that after controlling for time of day, accidents, and adjacent population density, group or peer effects have significant influence. This suggests that peer group level, as opposed to regional, performance measurement and monitoring is useful. Our research has significant implications for transportation system monitoring and planning.

Development of a Practical Method to Estimate the Eco-Level of Driver Performance

Motor vehicle’s fuel consumption is one of the main sources of energy consumption in road transportation and is highly influenced by driver performance in the process of driving. Eco-driving behavior has been proved to be an effective way to improve the fuel efficiency of vehicles. Essential to the efforts towards saving vehicle fuel is the need to estimate the eco-level of driver performance accurately and practically. Depending on on-board diagnostics and Global Position devices, individual vehicle’s instantaneous fuel consumption, engine revolution and torque, speed, acceleration, and dynamic location were collected. Back-propagation network was adopted to explore the relationship between vehicle fuel consumption and the parameters of driver performance. Taking 700 data samples in basic segments of urban expressways as our training set and 100 data samples as validation test, we found the optimal model structure and parameters through repeated simulation experiments. In addition to the average and standard deviation value, the fluctuation frequency of driver performance data was also viewed as influence factors in eco-level estimation model. The average estimation accuracy of our developed model has been tested to be 96.37%, which is quite higher than that of linear regression model. The study results provide a practical way to evaluate drivers’ performance from the perspective of fuel consumption and thus give basis for rewarding best drivers within eco-driving programs.